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Title: Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Abstract

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantificationmore » is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.« less

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; National Institutes of Health (NIH); National Institute of Environmental Health Sciences
OSTI Identifier:
1642077
Report Number(s):
PNNL-SA-151164
Journal ID: ISSN 1535-3893
Grant/Contract Number:  
AC05-76RL01830; UO1ES027292; R01 DK122160; P41GM103493
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Proteome Research
Additional Journal Information:
Journal Volume: 19; Journal Issue: 7; Journal ID: ISSN 1535-3893
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
quantitative proteomics; label-free quantification; large-scale; mass spectrometry; block design; common references

Citation Formats

Zhang, Tong, Gaffrey, Matthew J., Monroe, Matthew E., Thomas, Dennis G., Weitz, Karl K., Piehowski, Paul D., Petyuk, Vladislav A., Moore, Ronald J., Thrall, Brian D., and Qian, Wei-Jun. Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling. United States: N. p., 2020. Web. doi:10.1021/acs.jproteome.0c00310.
Zhang, Tong, Gaffrey, Matthew J., Monroe, Matthew E., Thomas, Dennis G., Weitz, Karl K., Piehowski, Paul D., Petyuk, Vladislav A., Moore, Ronald J., Thrall, Brian D., & Qian, Wei-Jun. Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling. United States. doi:10.1021/acs.jproteome.0c00310.
Zhang, Tong, Gaffrey, Matthew J., Monroe, Matthew E., Thomas, Dennis G., Weitz, Karl K., Piehowski, Paul D., Petyuk, Vladislav A., Moore, Ronald J., Thrall, Brian D., and Qian, Wei-Jun. Thu . "Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling". United States. doi:10.1021/acs.jproteome.0c00310.
@article{osti_1642077,
title = {Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling},
author = {Zhang, Tong and Gaffrey, Matthew J. and Monroe, Matthew E. and Thomas, Dennis G. and Weitz, Karl K. and Piehowski, Paul D. and Petyuk, Vladislav A. and Moore, Ronald J. and Thrall, Brian D. and Qian, Wei-Jun},
abstractNote = {Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.},
doi = {10.1021/acs.jproteome.0c00310},
journal = {Journal of Proteome Research},
issn = {1535-3893},
number = 7,
volume = 19,
place = {United States},
year = {2020},
month = {5}
}

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